We represent the prior and posterior beliefs as vectors of the probabilities of each syntactic category spanning w j i , and measure M ij , the amount of modification required, as the summed K-L divergence of the prior from the posterior vector.

if N is the set of nonterminals in the grammar, the size of the belief update is modeled as 1 In the remainder of the paper, we show how to compute M ij by using Bayesian inference on quantities calculated in ordinary probabilistic incremental Earley parsing with a stochastic context-free grammar (SCFG), and show that our model makes the correct predictions using an SCFG for English on the original local-coherences experiment of .


2 To calculate the prior, we first use Bayes rule to rewrite it as Recall that at this point, i and j do not refer to index positions in the actual string but rather serve to identify the substring w j i of interest.

p (w j i ) denotes the probability that at an arbitrary point in  There are at least j words an arbitrary sentence, the next j − i words will be w j i , and p (X k≥j i ) denotes the probability that an arbitrary point in an arbitrary sentence will be the left edge of a category X that spans at least j − i words.


Second, our model gives a rational basis to an effect which has typically been seen to result from irrationality of the human sentence processor.

the cost that our model describes of updating bottom-up prior beliefs to in-context posterior beliefs can be viewed as resulting from a rational process in the case that the bottom-up prior is available to the human sentence processor more rapidly than the in-context posterior.


Our first key idea is to view the oracle extraction as a bottom-up model scoring process on the hypergraph.

we train a 4-gram language model (LM) on only the reference translation(s), and use this LM as the only model to do a Viterbi search on the hypergraph to find the hypothesis that has the maximum (oracle) LM score.


This makes it difficult to incorporate semantic parsing into SMT merely by applying the sort of dynamic programming techniques found in current syntactic and tree-structured SMT models, most of which rely on being able to factor the computation into independent computations on the subtrees.

the key computational obstacle is that the semantic parse of a larger string (or string pair, in the case of translation) is not in general strictly mechanically composable from the semantic parses of its smaller substrings (or substring pairs).


Instead we try points on different search lines.

we test two additional points on the line through the highest point and the lowest point, and on the line through the reflection point and the lowest point.


However, our task differs from subjectivity analysis, since both bald as well as qualified claims can involve subjective language.

objective statements are always categorized as qualified claims, but subjective statements can be either bald or qualified claims.


Most spoken dialogue systems wait until the user stops speaking before trying to understand and react to what the user is saying.

in a typical dialogue system pipeline, it is only once the user's spoken utterance is complete that the results of automatic speech recognition (ASR) are sent on to natural language understanding (NLU) and dialogue management, which then triggers generation and synthesis of the next system prompt.


An interesting question is: Can we do semi-supervised speaker clustering?

can we make use of any available information that can be helpful to speaker clustering?


We pursue a different avenue in which we augment the basic acoustic features of every frame with those of the neighboring frames.

the acoustic features of the current frame and those of the K L frames to the left and K R frames to the right are concatenated to form a high-dimensional feature vector.


If we expand the L 2 norm terms of Equation 7, it is straightforward to show that Equation 7 is equivalent to Equation 5 provided that the graph weights are set to proper values, as follows.

by assigning appropriate values to the weights of the intrinsic and penalty graphs, the SDA optimization problem in Equation 5 can be solved within the elegant graph embedding framework.


The system recalculates the basic score based on the keyword chosen by the user.

the system multiplies the basic score by the coefficient β dependent on the keyword.


We describe experiments examining the optimal domain for accent analysis.

we compare pitch accent identification at the syllable, vowel or word level as domains for analysis of acoustic indicators of accent.


However, if we accept that the feature representations accurately model the acoustic information contained in the regions of analysis and that the BURNC annotation is accurate, the most likely ex-planation for the superiority of word-based prediction over syllable-or vowel-based strategiesis is that the acoustic excursions correlated with accent occur outside a word's lexically stressed syllable.

complex pitch accents in English are generally realized on multiple syllables.


In this paper, we face the problem of performing shallow semantic analysis of speech transcrip-tions from real-world dialogs.

we apply Support Vector Machines (SVMs) and Kernel Methods to the design of a semantic role labeler (SRL) based on FrameNet.


G is composed of nodes (v) which correspond to possible intermediate steps in the process of completing the specified task, and edges (e) which connect nodes.

v corresponds to dialog state to achieve domainspecific subtask in its expected agenda.


With the help of an efficient implementation, it avoids any translation candidate selection at the document level (Resnik and Smith, 2003;Smith, 2002;Snover et al., 2008;Utiyama and Isahara, 2003;Munteanu and Marcu, 2005;Fung and Cheung, 2004).

the extraction algorithm works when no document-level information is available.


We use L 1 regularization because we can obtain a sparse parameter vector, for which many of the parameter values are exactly zero.

learning with L 1 regularization naturally has an intrinsic effect of feature selection, which results in an efficient and interpretable inference with almost the same performance as L 2 regularization (Gao et al., 2007).


In this paper, we explore the use of personalization in the context of voice searches rather than web queries.

we focus on data from a multimodal cellphone-based business search application (Acero et al., 2008).


In this paper, we address the problem of predicting the perceived level of certainty of a spoken utterance.

we have a corpus of utterances where it is possible to isolate a single word or phrase responsible for the speaker's level of certainty.


In this study we investigate a variation of entitycentric summarization where the goal is not to summarize information about a single entity, but pairs of entities.

our aim is to jointly generate two summaries that highlight differences between the entities -a task we call contrastive summarization.


In our case we have 1 + |A T | distributions p(a), and p(•|a) for all a ∈ A T .

we then define L: the SAM summarizer prefers summaries whose induced model is close to the model induced for all the opinions about the product of interest.


Comparing the summaries only to the products' full language models prevents us from rewarding summaries that convey a false contrast between the products under comparison.

we now optimize the following joint summary score: Note that we could additionally model divergence between the two summaries (i.e., merging models in figures 1b and c), but such modeling is redundant.


We have shown how a three-component model that consists of a model of the phenomenon being studied and two distributional components, one from the source data and one from the target data, allows one to create data artificially for training a semantic parser.

analysis and minimal annotation of only a small subset of utterances from the target domain of spoken dialogue systems suffices to determine a model of NSUs as well as the necessary target distribution.


Moreover, it appears that novice users don't have much difficulty generating useful NPRs on their own; we expect that their skill would increase with use.

useful would be for the system to be able to provide feedback, including generating NPRs; we have started investigating this reverse problem, of obtaining NPRs from pronunciations, and are encouraged by the initial results.


In contrast to previous work, this paper advocates that the AL scenario brings out the need to modify PL approaches to dealing with imbalance.

a new method is developed for cost-weighted SVMs that estimates a cost model based on overall corpus imbalance rather than the imbalance in the so far labeled training data.


In this paper we present a policy-based error analysis approach that demonstrates a limitation to the current commonly adopted paradigm for sentence compression (Knight and Marcu, 2000;Turner and Charniak, 2005;McDonald, 2006;Clark and Lapata 2006).

in typical statistical compression approaches, a simplifying assumption is made that compression is accomplished strictly by means of word deletion.


In this paper, we propose the use of language modeling methodologies for Answer Validation, using corpus-based methods that do not require the use of external sources.

we propose a model for Answer Credibility which quantifies the reliability of a source document that contains a candidate answer and the Question's Context Model.


we propose the development of an Answer Credibility score which quantifies reliability of a source document that contains a candidate answer with respect to the Question's Context Model.


With increasing quantities of text and transcribed speech available online, the ability to categorize documents based on characteristics beyond topic becomes ever more important.

the genre of a document -whether it is a news report or an editorial, a speech transcript or a weblog -may be relevant for many human tasks.


For many studies, genre has been associated with categories of text, such as research article, novel, news report, editorial, advertisement, etc.

several studies use classes identified in the Brown corpus or the British National Corpus.


Bunt (2006) shows that this does not always lead to a notion of dimension that has any conceptual and theoretical significance, and argues that some of the function classes of DAMSL do not constitute proper dimensions.

a theoretically grounded multidimensional schema should provide an account of the possible multifunctionality of dialogue utterances.


In this paper, we propose the use of metadata contained in documents to improve coreference resolution.

we quantify the impact of speaker and turn information on the performance of our coreference system, and show that the metadata can be effectively encoded as features of a statistical resolution system, which leads to a statistically significant improvement in performance.


Second, poor feature choices can hurt classification.

adding a feature that matches a list of abbreviations can increase the error rate; using the list ("Mr.", "Co.") increases the number of errors by up to 25% in our experiments.


Alternately, by adding 16 filters, we can raise the F1 to 91.60.

adding filters is nearly as effective as our manual feature engineering.


The motivation for this research is the investigation of the interaction between information structure and word order in Tagalog.

we wish to determine the utility of discourse-based contextual information in predicting word order in Tagalog, in a natural language generation context.


In this work, we derive this matrix from broadcast news development data.

two systems: HMM based automatic speech recognition (ASR) (Chaudhari and Picheny, 2007) and a neural network based acoustic model (Kingsbury, 2009), are used to ana-lyze the data and the results are compared to produce confusion estimates.


A feature of the systems developed for meeting recognition is the use of multiple recognition passes, cross-adaptation and model combination (Hain et al., 2007).

successive passes make use of more detailed-and more diverse-acoustic and language models.


The triples are extracted relative to a set of putative token instances for each of the intransitive and transitive valences for a given VPC.

a given triple should be classified as positive if and only if it is associated with at least one noncompositional token instance in the provided tokenlevel data.


Techniques like self-training (SELF) and system combinations (COMBO) can further improve parsing accuracies, but are also orthogonal to our work.

the COMBO methods seem related to our work, but are very different in their nature.


mixed parsing models evaluated on the target domain) and predict their f-scores with our model.

for this measure, we are interested in the point with the highest predicted f-score.


Some of these approaches identified the problem of polysemy and applied sense disambiguation with respect to WordNet in order to capture the different senses of a target term (Navigli and Velardi, 2004b;Navigli and Velardi, 2004a).

the taxonomy built by exploiting head/modifiers relations was modified according to WordNet's hyponymy relations between senses of disambiguated terms.


The comparison of these taxonomies is based on the semantic cotopy of a node, which has also been used in (Maedche and Staab, 2002;Cimiano et al., 2005).

the semantic cotopy of a node is defined as the set of all its super-and subnodes excluding the root and including that node.


Our model exploits the redundancy inherent in this multimodal collection.

we assume that the images and their surrounding text have been generated by a shared set of topics.


We explored the parameter space on held-out data.

we fit the parameters for the word association and similarity models separately using a third of the association norms and WordSim353 similarity judgments, respectively.


This research seeks to fill the gap between topic evaluation in computational linguistics and machine learning, in developing techniques to perform intrinsic qualitative evaluation of learned topics.

we develop methods for evaluating the quality of a given topic, in terms of its coherence to a human.


Despite differences in detail, the vast majority of syntactic methods share a critical dependence on word alignments.

they infer syntactic correspondences between the source and target languages through word alignment patterns, sometimes in combination with constraints from parser outputs.


In this paper, we remove the dependence on word alignments and instead directly model the syntactic correspondences in the data, in a manner broadly similar to Yamada and Knight (2001).

we propose an unsupervised model that aligns nodes of a parse tree (or forest) in one language to spans of a sentence in another.


For efficiency, we also ruled out span alignments that are extremely lopsided, for example, a 1-word English span aligned to a 20-word foreign span.

we pruned any span alignment in which one side is more than 5 times larger than the other.


We found in early development found that removing low-count rules improved translation performance substantially.

we settled on the following scheme: we kept all rules with a single foreign terminal on the right-hand side.


While that parser uses heavily refined PCFGs with rule probabilities defined at the refined symbol level, we interact with its posterior distribution via posterior marginal probabilities over unrefined symbols.

to each unrefined anchored production i A j → i B k C j , we associate a single feature whose value is the marginal quantity  We begin with the same set of alignment features as Haghighi et al.


One reason why we have low accuracies is that our gold standard based evaluation is not established on real gold standards.

we don't have gold standards in terms of translation boundary since different translations generate very different translation boundaries.


Therefore we approximate H with a graph H that only contains nodes "near" to the node representing the query phrase.

we perform breadthfirst search starting from the query node up to a depth d, or until the number of nodes visited in the search has reached a maximum of n nodes.


Unlike previous work (e.g., (Sjöbergh and Knutsson, 2005;Brockett et al., 2006;Foster and Andersen, 2009)), we selectively introduce mistakes in an appropriate proportion.

to create training data that closely resemble text with naturally occurring errors, we use error frequency information and error distribution statistics obtained from corrected non-native text.


As the table shows, the distribution of articles is quite different for native data (Wikipedia) and non-native text.

non-native data have a lower proportion of the.


both for training (native data) and test (ESL data), source denotes the form that the classifier sees as a feature (which could be an error) and label denotes the correct article.


Method 2: ArticleDistrBeforeAnnot We use the distribution of articles in the ESL data before the annotation to change the distribution of articles in the training.

we change the articles so that their distribution approximates the distribution of articles in the ESL data.


For example, in the Czech annotated subcorpus, label the corresponds to source the in 85% of the cases and corresponds to source None in 14% of the cases.

in 14% of the cases where the article the should have been used, the writer used no article at all.


Finally, we are only considering cases in which the system suggests a correction.

we do not count as correct instances where the system's prediction matches a correct preposition/article.


To make event detection feasible on web-scale corpora, we present an algorithm based on locality-sensitive hashing which is able overcome the limitations of traditional approaches, while maintaining competitive results.

a comparison with a stateof-the-art system on the first story detection task shows that we achieve over an order of magnitude speedup in processing time, while retaining comparable performance.


The number of hyperplanes k can be considered as a number of bits per key in this hashing scheme.

if for document x and hyperplane vector u i , we set the i-th bit to 0, and 1 otherwise.


(2004), where any 3L documents were used, we compare to the 3L documents that collide most frequently with the new document.

if S is the set of all documents that collided with a new document in all L hash tables, we order the elements of S according to the number of hash tables where the collision occurred.


This way, we can test if our system is on par with the best existing systems, and also accurately measure the speedup that we get over a traditional approach.

we compare our system with the UMass FSD system (Allan et al., 2000).


As was mentioned in Section 2.2, simply using LSH to find a nearest neighbor resulted in poor performance and a high variance of results.

the mean normalized cost of ten runs of our system without the variance reduction strategy was 0.88, with a standard deviation of 0.046.


It is clear that UMass outperforms our system, but the difference is negligible.

the minimal normalized cost C min was 0.69 for the UMass system, and 0.71 for our system.


Since lattices contain all the likely hypotheses (unlikely hypotheses are pruned during recognition and will not be included in the lattice), conditional entropy for any given input speech signal, x, can be approximated by the conditional entropy of the lattice.

where L i is the corresponding decoded lattice (given speech recognizer parameters) of utterance x i .


System B using this class set performs worse than System A by 1.7% absolute (See Table 4).

the overformatting rate (OFR) is higher by 1.2% absolute in System B than in System A.


1 We rank the extractions in U R according to how similar their arguments' contextual distributions, P (c|e i ), are to those of the seed arguments.

extractions are ranked according to: where KL represents KL Divergence, and the outer sum is taken over arguments e i of the extraction e. For HMMs, we alternatively rank extractions using the HMM state distributions P (t|e i ) in place of the probabilistic context vectors P (c|e i ).


It also suggests that performance over shorter documents appears to be the dominating factor in the overall ranking of the different methods.

cOS 1NN and SVM appear to be able to classify shorter documents most reliably, leading to the overall result of them being the best-performing methods.


The slice sampler uniformly finds much better solutions than the Gibbs sampler regardless of initialisation.

the slice sampled model initialised with the naive LB structure achieves a higher likelihood than the M4 initialised model, although this is not reflected in their relative BLEU scores.


2 These descriptions are less straightforward than those for the enclitics.

the description of á-∼ áw-as "absolutive" is misleading.


Regarding that prefix, R&R write, "...this pronominal marks subjects in intransitive clauses when they are possessors, and objects in transitive clauses when the subject is first or second person."

(675) it does not occur in all transitive clauses, and only in those intransitive clauses where the subject is possessive.


It has long been appreciated that monolingual parsing computes the intersection of an FSA and a CFG (Bar-Hillel et al., 1961;van Noord, 1995).

if S is an FSA encoding some sentence s, intersection of S with a CFG, G, results in a parse forest which contains all and only derivations of s, that is L(S) ∩ L(G) ∈ {{s}, ∅}.


It has long been appreciated that parsing computes the intersection of an FSA and a CFG (Bar-Hillel et al., 1961;van Noord, 1995;Grune and Jacobs, 2008).

parsing an FSA, S, with a CFG, G, results in a parse forest which contains derivations of strings in I = L(S) ∩ L(G), 1 and which may be ∅.


From this, we conclude that an LCFRS of fanout ϕ in which all composition operations are either concatenation operations, wrapping operations, or operations of rank 0 or 1, can be parsed in time O(|w| 2ϕ+2 ).

nullary and unary composition operations do not affect this worst-case complexity, since their associated deduction steps can never have more than 2ϕ indexes.


Through a utility evaluation on summary writing we have proved that IE techniques, especially cross-document IE, can aid news browsing, search and analysis.

temporal event tracking across documents helps users perform better at fact-gathering than they do without IE.


We use linear regression to directly predict the opening weekend gross earnings, denoted y, based on features x extracted from the movie metadata and/or the text of the reviews.

given an input feature vector x ∈ R p , we predict an outputŷ ∈ R using a linear model:ŷ = β 0 + x β.


A combination of the meta and text features achieves the best performance both in terms of MAE and r. While the text-only models have some high negative weight features, the combined models do not have any negatively weighted features and only a very few metadata features.

the text is able to substitute for the other metadata features.


We propose a simple extension to MMR: at each step, we interactively ask the user to select the best sentence for inclusion in the summary.

instead of the system automatically selecting the candidate with the highest score, it presents the user with a ranked list of candidates for selection.


Narrative: The analyst would like to know of efforts to curtail the transport of drugs from Mexico to the U.S.

the analyst would like to know of the success of the efforts by local or international authorities.


In the second method, we augment features with hypotheses obtained from classifiers trained using other domain data.

we first trained 5 classifiers from 5 review domain data sets respectively, and encoded the hypotheses from different classifiers as features for blog training (together with the original features of the blog data).


The motivation behind such proposals have been the empirical findings that "weak" discourse relations such as ELABORATION are the most common type of relations, and that a large percentage of adjacent sentences in fact do not have any entities in common.

a corpus based evaluation of Centering theory found that only 42% of pairs of adjacent sentences have at least one entity in common and hypothesized that discourse relations are responsible for creating local coherence in the remaining cases (Poesio et al., 2004).


We combine two recently released corpora: discourse relations from the Penn Discourse Treebank and coreference annotations from the OntoNotes corpus, to assess the prevalence and interplay between factors that create local coherence in newspaper text.

in our study we examine how three hypotheses formulated in prior work play out in the Wall Street Journal texts in our corpus: Hypothesis 1 Adjacent sentences that do not share entities are related by non-elaboration discourse relations [Poesio et al.


To this aim, we started by adopting the basic approach previously discussed.

starting from an English/French corpus of T-H pairs, we automatically translated each H fragment from French into English.


Overall, our work sets a novel framework for further studies and experiments to improve crosslingual NLP tasks.

cLTE can be scaled to more complex problems, such as cross-lingual content merging and synchronization.


The window-wise co-occurrence statistic is an even narrower notion of context, considering only terms in a window surrounding w .

a window of a fixed size is moved along the text, and f ww is set as the number of times both w and w appear in the window.


In this work, we use crowdsourcing to generate evaluation data to validate simple techniques designed to adapt a widely-used high-performing named entity recognition system to new domains.

we achieve a roughly 10% improvement in precision on text from the information technology (IT) business press via post hoc rule-based error reduction.


In this paper we re-examine query ambiguity in connection with searcher clickthrough behavior.

we posit that clickthrough information could provide important evidence for classifying query ambiguity.


In this study we focus on two orthogonal query intent dimensions, adapted from the top level of user goal taxonomies such as (Rose and Levinson, 2004).

a query could be ambiguous or unambiguous; as well as informational or navigational.


The features described here can then be used as input to many available classifiers.

we use the Weka toolkit 1 , as described below.


Notice that values for ρ range from -1 to 1, with +1 indicating perfect rank correlation, -1 perfect inverse correlation, and 0 no correlation.

for a fixed X, the best-correlated Y is that for which ) , ( Y X ρ is highest.


On that portion, we evaluate how closely the Turker matches the LDC editor, and weight them accordingly when predicting the number of edits of the rest of that group's segments.

the Turker's weight is the absolute difference between the Turker's edit count and the professional editor's edit count.


Rather, we are interested in mirroring the professional translator's edit rate.

the closer a Turker's edit rate is to the LDC editor's, the more we should prefer the worker.


This can be detected by not limiting our verification to comparing behavior to the professional editor's, but also by comparing submitted edits to the MT output itself and to the human reference.

a worker's submission could be characterized in terms of its distance to the MT output and to the human reference, thus building a complete 'profile' of the worker, and adding another component to guard against poor data quality and to reward the desired behavior.


Then a simple method of exact matching can be used to select the product attribute candidates from the test set.

for a syntactic structure 3 T in the test set, if T exactly matches with one of the standard syntactic structures, then its corresponding string can be treated as a product attribute candidate.


Here we batch or incrementally retrain using a sliding window approach where the training set size (the number of sentence pairs) remains constant.

we ensured that we used the same number of sentences as the baseline.


wikis), and sharing of multimedia content, and entertainment.

social media have today become also an important market for advertisement as well as a global forum for consumer opinions (Kushal et al., 2003).


We propose a methodology to develop a practical Machine Transliteration system between any two nodes of the above graph, provided a two-step path exists between them.

even when no parallel data exists between X & Y but sufficient data exists between X & Z and Z & Y it is still possible to develop transliteration functionality between X & Y by combining a X → Z system with a Z → Y system.


A significant shortcoming of all the previous works was that none of them addressed the issue of performing transliteration in a resource scarce scenario, as there was always an implicit assumption of availability of data between a pair of languages.

none of the above approaches address the problem of developing transliteration functionality between a pair of languages when no direct data exists between them but sufficient data is available between each of these languages and an intermediate language.


Thus, The probabilities P (Y |Z) and P (Z|X) in Equation (3) are derived from the two stages of the bridge system.

we assume that the parallel names corpora are available between the language pair, X and Z, and the language pair, Z and Y .


The underlying assumption in transitive transliteration systems (as expressed in Equation 3), is that "Y is independent of X given Z".

we assume that the representations in the language will Z "capture sufficient transliteration information from X to produce correct strings in Y ".


A number of recent articles in computational linguistics venues called for a closer examination of the type of noise present in annotated datasets used for benchmarking (Reidsma and Carletta, 2008; Beigman Klebanov and Beigman, 2009).

beigman Klebanov and beigman articulated a type of noise they call annotation noise and showed that in worst case such noise can severely degrade the generalization ability of a linear classifier (beigman and beigman Klebanov, 2009).


We might however ask whether a different model could provide a similarly fitting explanation.

heterogeneity among annotators has been seen as a major source of noise in the aggregate data and there are several works attempting to separate high quality annotators from low quality ones (Raykar et al., 2009;Donmez et al., 2009;Sheng et al., 2008;Carpenter, 2008).


Figure 6 shows that model M=B1+B2+B3 does not fit well, as SRTE dataset exhibits a rather different distribution from both BRTE datasets.

it is clear that had a model been fitted on SRTE data, the coin flipping probabilities for the clear types, B1 and B3, would have to be moved towards 0.5; that is, an average annotator in SRTE dataset had worse ability to detect clear 0s and clear 1s than an average BRTE annotator.


Our overall plan is to give a generic algorithm for Bayesian training that is a "drop-in replacement" for EM training.

we input an FST cascade and data and output the same FST cascade with trained weights.


2 For the distributed case, consider setting µ i,n = k i,n /k n , where k n = i k i,n .

we mix parameters proportional to the number of errors each made during the previous epoch.


the iterative parameter mixing strategy has a higher final f-measure than training on all the data serially than the standard perceptron (f-measure of 87.9 vs. 85.8).


The same trends are seen for dependency parsing that are seen for named-entity recognition.

iterative parameter mixing learns classifiers faster and has a final accuracy as good as or better than training serially on all data.


Our basic hypothesis is that recalculating p(z ij |z, x, α, β) to take the information content of each token into account will improve the results of LDA.

we have incorporated a weighting term into Eq.


The first, called Edit1 (E1), employs a unit cost function for each of the three operations.

given 1 The values are computed from Web counts.


Let q s = q s \ q t and q t = q t \ q s , where \ is the set difference operator.

the co-occurrence count of two terms w i and w j from a query pair q s , q t is denoted by n i,j (q s , q t ) and is defined as: if a term occurs in both queries, it has a co-occurrence count of 1.


This paper address the problem of entity linking.

given an entity mentioned in unstructured texts, the task is to link this entity with an entry stored in the existing knowledge base.


Typically, ESL/EFL users are reasonably good at reading and extracting relevant information from English content but not so good at expressing their information needs in English.

getting the spellings of foreign names in English correctly is very difficult for most ESL/EFL users due to the differences in the way a foreign name is pronounced in the native languages.


The basic idea is that learning a vocabulary that both meets the constraints of the word-learning task and is consistent with the objective of the segmentation task can yield a better segmentation.

we hope to find a synergy in the joint inference of meaning and segmentation.


On all subsets, the full joint model outperforms the other two variants.

the greatest gain is for the referring nouns, with a 21% reduction in error.


Decisions about more sophisticated encoding of nonlinguistic information may thus require more knowledge about children's representations of the world around them We find (1) that it is possible to jointly infer both meanings and a segmentation in a fully unsupervised way and (2) that doing so improves the segmentation performance of our model.

we found that although the word learning side suffered from segmentation errors, and performed worse than a model that learned from a gold standard segmentation, the loss was only slight.


For these reasons, we attempt to predict as many diacritics as possible.

we predict the diacritics whose unicode values are 05B0-B9, 05BB-BD, 05BF, 05C1-C2, and 05C4.


The hybrid nature of hybrid naturally raises the question of whether or not the inner consonant model should be trained only on rare words or on all of the data.

is the distribution of diacritics different in rare words?


In this paper, we also focus on the IMT framework.

we present an IMT system that is able to learn from user feedback.


The log-linear model, which includes the above described feature functions, is used to generate the suffix e s given the user-validated prefix e p .

the IMT system generates a partial phrasebased alignment between the user prefix e p and a portion of the source sentence f , and returns the suffix e s as the translation of the remaining portion of f (see (Ortiz-Martínez et al., 2009)).


Alternatively, we carried out experiments in a different learning scenario.

the XEROX  test corpora were interactively translated from the English language to the other three languages, comparing the performance of a batch IMT system with that of an online IMT system.


First, the multinomial distributions θ for a PCFG based on G are drawn from Dirichlet distributions.

multinomial θ A ∼ Dir(α A ) where α is collection of Dirichlet parameters, indexed by A ∈ N. Trees are then generated top-down starting with S. Any non-adapted nonterminal A ∈ N \ M is expanded by drawing a rule from R A .


We will use b to exploit the ideas from Section 2.

let b = {b s } s∈S be a collection of binary variables indexed by a set of sites S. • HMM: If the HMM has K = 2 states, S is the set of positions in the sequence.


• HMM: z −s includes all but the transitions into and out of the state at s plus the emission at s. • USM:  A token-based sampler considers one site s at a time.

we evaluate the likelihoods of z s:0 and z s:1 according to (3) and sample b s with probability proportional to the likelihoods.


4 Sampling Non-Binary Representations We can sample in models without a natural binary representation (e.g., HMMs with with more than two states) by considering random binary slices.


We show how features can easily be added to standard generative models for unsupervised learning, without requiring complex new training methods.

each component multinomial of a generative model can be turned into a miniature logistic regression model if feature locality permits.


In this paper, we present a range of experiments wherein we improve existing unsupervised models by declaratively adding richer features.

we parameterize the local multinomials of exist-ing generative models using features, in a way which does not require complex new machinery but which still provides substantial flexibility.


We attempt the most difficult version of this task where the only information our system can make use of is the unlabeled text itself.

we do not make use of a tagging dictionary.


(2001b) embedded information in the tree structure of the text by adjusting the structural properties of intermediate representations of sentences.

instead of performing lexical substitution directly to the text, the secret message is embedded into syntactic parse trees of the sentences.


Since we want to embed secret bits 101 in u 1 , u 2 and u 3 respectively, the embedding unit u 2 should contain no paraphrasable sentence.

the paraphrasable phrase in t 4 should be replaced by its paraphrase.


Randomly permuting the training corpus did not change our results significantly, so we believe our results are not greatly dependent on the initial sequence order.

instead of assigning substitution costs, we compute the cost of aligning a word into a particular column, as follows:  the probability of feature j from class i occurring in column k is a simple maximumlikelihood estimate -count the number of times we have already aligned that feature in the column and divide by the number of sequences aligned.


These rules encode a substantial amount of linguistic knowledge about the long distance dependencies prevalent in questions, which would be challenging to learn from existing corpora of questions and answers consisting typically of only thousands of examples (e.g., Voorhees, 2003).

the following sequence of transfor-During the Gold Rush years in northern California, Los Angeles became known as the "Queen of the Cow Counties" for its role in supplying beef and other foodstuffs to hungry miners in the north.


Surface Features This configuration includes only features that can be computed from the surface form of the question, source sentence, and answer phrase-that is, without hidden linguistic structures such as parts of speech or syntactic structures.

it includes features for lengths, length histograms, WH words, negation, and language model likelihoods.


A fundamental problem for WSD is that the required knowledge is open-ended.

for every ambiguous term, new kinds of information about the world become important, and the knowledge that a system may have acquired for previously-studied ambiguous terms may have little or no impact on the next ambiguous term.


Our approach focuses on English multiword expressions that appear as sequences in text.


ferent from the shallow parser output after the replacement, we came across a number of instances that were classified in more than one class of the previous subsection.


7.20% of these change instances are due to one or more parts of speech changes, and are classified to change class PoS.

in 7.20% of cases where there is a difference between the shallow parses before and after replacing the multiword expression tokens there is one or more tokens that were assigned a different part of speech.


6 However, it is not clear that this step is necessary.

how many sentences are not wellformed if one uses a simple word-by-word voting scheme?


Thus, the final dataset consists of pairs of message pairs ((p i , p j ), (p i , p k )), where they have the same reply message p i , and p j is the correct quote message of p i , but p k is not.

(p i , p j ) is considered as a positive example; (p i , p k ) is a negative example.


Our pairwise ranking model 1 takes as input an ordered pair of message pairs ((p i , p j ), (p i , p k )) and computes their relatedness using a similarity function sim.

( where x ij is the similarity value between post p i and p j ; x ik is the similarity value between post p i and p k .


Our finding is that model performances vary by subset.

we find that it is only in cases where the positive example has low lexical cohesion (e.g.


Adjacent to the root node are chains of common sequences of words found within the input sentences.

each word is represented by a node and an associated count that indicates how many times the node's phrase occurs within the set of input sentences.


After the graph is constructed, the PR algorithm assigns a weight to every node in order to prevent longer phrases from dominating the output.

stop words are given a weight of zero while remaining words are given weights that are both proportional to their count and penalized the farther they are from the root node: In the above equation, the RootDistance of a node is simply the number of hops to get from the node to the root node and the logarithm base, b, is a parameter to the algorithm.


Due to the lack of human generated annotations, we employ an unsupervised strategy.

we apply TFIDF ranking and TextRank (Mihalcea and Tarau, 2004) keyword extraction on Twitter messages after a series of text preprocessing steps.


In the future, we will investigate knowledge-richer methods for segmentation.

we will investigate whether an automatic vocalization step previous to segmentation will improve POS tagging accuracy for unknown words.


We approach this problem by treating g2p as a function from contiguous sequences of graphemes, which we call 'grapheme subwords', to sequences of phonemes ('phoneme subwords'), so that there is minimal ambiguity in finding the phoneme subword that corresponds to a given grapheme subword.

we seek to minimize both these quantities: 1.


Consider a lexicon of grapheme subwords G and phoneme subwords P that is extracted from a dictionary of word-pronunciation pairs, along with a joint probability distribution over G and P. As stated earlier, our objective is to minimize the entropy of phoneme subwords conditioned on a given grapheme subword, as well as the entropy of the grapheme subwords.

we would like to minimize H(P|G) + H(G), which is This objective can be restated as minimizing the expected description length of the lexicon, which is given by its entropy.


In order to correct for this, we perform a non-linear transformation on the local CRF posterior representation before applying a KL-transform and subsequent stages.

we normalize all of the weights λ j and µ j in Equation 1 by a fixed positive constant n to obtain normalized weights λ j and µ j .


We note that the probability of a label sequence computed using the transformed weights, p (Q|X), is equivalent to taking the nth-root of the CRF probability computed using the unnormalized weights, with a new normalization term Z (X) where, p(Q|X) is as defined in Equation 1.

also observe that the monotonicity of the nth-root function ensures that if the rank order of the n-best phone recognition results are not impacted by this change.


We use an improved 10 L method (Salvador and Chan, 2004) to automatically locate this area of diminishing returns.

we determine its end-points [k 0 , k * ] by minimizing squared error, estimatingk 0 = 7 and k * = 15.


Formally, given our set of factors F and an observed sentence W , marginal inference amounts to calculating the probability µ F i that our binary features φ i are active.

for each factor Ψ i For compactness, we follow the convention of Wainwright and Jordan (2008) and represent the belief for a variable using the marginal probability of its corresponding unary factor.


It is easy to show (see Appendix) that the following holds: Proposition 1.

the gain of a factor Ψ i with respect to the sub-graph F ⊆ F is the gain of a factor Ψ i depends on two properties of Ψ i .


While this approach does tackle marginalization, it is focused on improving its accuracy.

the optimization problems they solve in each iteration are in fact larger than the problem we want to relax.


Rambow and Satta (1999) categorize the generative capacity of LCFRS grammars according to their rank and fan-out.

they show that grammars can be arranged in a two-dimensional grid, with languages of rank r and fan-out f having greater generative capacity than both grammars of rank r and fan-out f − 1 and grammars of rank r − 1 nmod sbj root vc pp nmod np tmp A hearing is scheduled on the issue today Figure 4: A dependency tree with the LCFRS rules extracted for each word (Kuhlmann and Satta, 2009).


This classification is somewhat unsatisfying because minor changes to a grammar can change both its rank and fan-out.

through factorizing rules, it is always possible to decrease rank, potentially at the cost of increasing fan-out, until a binarized grammar of rank 2 is achieved.


The output is a polarity vector pol ∈ R |V | such that pol i is the polarity score for the i th candidate phrase (or the i th node in G).

we desire pol to have the following semantics: > 0 i th phrase has positive polarity < 0 i th phrase has negative polarity = 0 i th phrase has no sentiment Intuitively, the algorithm works by computing both a positive and a negative polarity magnitude for each node in the graph, call them pol + i and poli .


Since this graph encodes co-occurrences over a large, but local context window, it can be noisy for our purposes.

we might see a number of edges between positive and negative sentiment words as well as sentiment words and non-sentiment words, e.g., sentiment adjectives and all other adjectives that are distributionally similar.


The intuition behind label propagation seems justified.

if a node has multiple paths to a seed, it should be reflected in a higher score.


w, h, s respectively denote the sequence of For the example sentence in Figure 1 We define the joint probability distribution of the sentiment polarities of dependency subtrees s, given a subjective sentence w and its dependency tree h, using loglinear models: where is the feature function of the i-th phrase, and is classified to node feature which considers only the corresponding node, or edge feature which considers both the corresponding node and its head, as follows: where K n and K e respectively represent the sets of indices of node features and edge features.

let us consider how to infer the sentiment polarity p ∈ {+1, −1}, given a subjective sentence w and its dependency tree h. The polarity of the root node (s 0 ) is regarded as the polarity of the whole sentence, and p can be calculated as follows: the polarity of the subjective sentence is obtained as the marginal probability of the root node polarity, by summing the probabilities for all the possible configurations of hidden variables.


We used morphological information and explicit negation to find pairs of opposite polarity.

adjective pairs which were distinguished only by one of the prefixes 'un', 'in', 'dis', 'non', or by the negation marker 'not-' were selected for the seed set.


9 We implemented our system as an extension to the Alchemy system (Kok et al., 2009).

we developed an efficient parallelized implementation of our stochastic gradient descent algorithm using the message-passing interface (MPI).


10 Both trigger and argument-edge detections leave much room for improvement.

the system proposed many incorrect regulation triggers, partly because regulation triggers have the most variations  among all types.


Our system trailed UTurku due to a somewhat lower recall, but substantially outperformed all other systems.

it reduced F1 error by more than 10% compared to the previous best joint approach by Riedel et al.


We then make use of both PICO query and document structure to extend the classical language modeling approach to IR.

we investigate how each element of a PICO query should be weighted and how a location-based weighting strategy can be used to emphasize the most informative parts (i.e.


Our model exploits the redundancy inherent in this multimodal dataset by assuming that images and their surrounding text are generated by a shared set of latent variables or topics.

we describe documents and images by a common multimodal vocabulary consisting of textual words and visual terms (visiterms).


We also assume that the keywords are conditionally independent of each other: Since I and D are represented jointly as the concatenation of textual and visual terms, we may intuitively simplify the problem and use the mixed document representation d Mix directly in estimating the conditional probabilities P(w t |I, D): Substituting Equation (6) into (5) yields: As mentioned earlier, we assume that the image and its associated text are generated by a mixture of latent topics which we infer using LDA.

we select the number of topics K and apply the LDA algorithm to a corpus consisting of documents {d Mix } in order to obtain the multimodal word distributions over topics P(w|z 1:K ), and the estimated posterior of the topic proportions over documents P(z 1:K |d Mix ).


2 The basic idea is that for the source sentence, f, that is to be translated, we want to create a (monolingual) context-free grammar F that generates strings (f ) of words in the source language that are permutations of the original sentence.

this forest should contain derivations that put the source words into an order that approximates how they will be ordered in the grammar of the target language.


We want to construct an MBOT M such that M (t, u) = M (t, u) • G(t) for every t ∈ T Σ and u ∈ T ∆ .

each input tree should be rescored according to G; in the unweighted case this yields that the translation of M is filtered to the set of input trees accepted by G. We occasionally write the pair (a, b) in angled parentheses (' ' and ' ').


We extend the rewrite semantics (see Definition 2) to trees that include symbols foreign to a MBOT.

we (virtually) extend the input and output alphabets to contain all used symbols (in particular also the states of another MBOT).


Before we can extend it to the weighted case, we need to make sure that the sum in the definition of composition is finite.

we achieve this by requiring that • for every t ∈ T Σ and s ∈ S 1 there are finitely many u ∈ T ∆ such that t M may not have cyclic input ε-rules or M may not have cyclic output ε-rules.


The transformation component is still ex-tractive in nature, but the sentences that are selected in the transformation stage correspond to objects in the ontology and the properties linking them.

these are triples of the form < participant, relation, entity > where a participant is a person in the conversation, an entity is an item under discussion, and a relation such as positive opinion or action item links the two.


More importantly, our sentence weight u j is the sum of all the posterior probabilities for sentence j derived from the various sentence-level classifiers.

sentences are weighted highly if they correspond to multiple object properties in the ontology.


Table 2 shows the weighted recall scores and several conversation features that were weighted most highly by the logistic regression model.

we found that the email threads that yielded good performance tended to feature more active participants (# Participants), were not dominated by a single individual (BEGAUTH), and featured a higher number of turns (# Turns) that followed each other in quick succession without long pauses (PPAU, pause as percentage of conversation length).


In particular, we found that the email threads that yielded good performance tended to feature more active participants (# Participants), were not dominated by a single individual (BEGAUTH), and featured a higher number of turns (# Turns) that followed each other in quick succession without long pauses (PPAU, pause as percentage of conversation length).

these emails were structured more similarly to meetings.


Consider a set function f : (Lovasz, 1983)  An equivalent definition of submodularity is the property of diminishing returns, well-known in the field of economics.


The budgeted constraint arises naturally since often the summary must be length limited as mentioned above.

the budget B could be the maximum number of words allowed in any summary, or alternatively the maximum number of bytes of any summary, where c i would then be either number of words or the number of bytes in sentence i.


When |S| is small, f MMR is monotone and our theoretical results still hold.

assume that all edge weights are bounded: w i,j ∈ [0, 1] (which is the case for cosine similarity between non-negative vectors).


(3) as an ILP by introducing indicator (binary) variables x i,j , y i,j , i ̸ = j and z i for i, j ∈ V .

z i = 1 indicates that unit i is selected, i.e., i ∈ S, x i,j = 1 indicates that i ∈ S but j / ∈ S, and y i,j = 1 indicates both i and j are selected.


The last inequality follows from e −x ≥ 1 − x.

in all cases, we have when r = 1, we obtain the constant approximation factor, i.e.


By treating such context word pairs as single dimensions, the vector space can represent target words from both languages, counting the target words' co-occurrences with the context words from the respective language.

a sourcetarget word pair (s, t) will be assigned similar vectors in the semantic space if the context words of s are translations of the context words of t. Cross-lingual semantic similarity between words can be measured using standard vector space similarity (Lee, 1999).


3), notably without the use of syntactic data.

the results for the POBJs, notoriously difficult to model monolingually, are higher than for SUBJs or DOBJs.


Therefore, we approximate the performance of a supervised system by clustering instances according to their gold standard role after introducing some noise.

we randomly selected 5% of the gold standard roles and mapped them to an erroneous role.


The fact that it does not, indicates that the baseline error rate is higher precisely on these instances.

the model can help in detecting alternate linkings and thus baseline errors.


We further analyzed our model's ability to detect alternate linkings.

if we assume a standard linking where model and observation agree and an alternate linking where they disagree, we obtain the following.


Nevertheless, we made a good-faith effort to evaluate our system using their evaluation setting.

we ran our system on the same test set, Section 23 of the Penn Treebank (annotated with PropBank roles), using gold standard parses with six clusters for each verb type.


We also describe the application of our algorithms to machine translation and report the results of experiments with several translation data sets which demonstrate a substantial speed-up.

our results show a speed-up by two orders of magnitude with respect to the original method of Tromble et al.


Note that in practice, for numerical stability, all of these computations are done in the log semiring which is isomorphic to (R + ∪ {+∞}, +, ×, 0, 1).

the maximum weight path in the last step is then obtained by using a standard single-source shortest-path algorithm.


This opens up the option of seeking the most appropriate rational kernel or transducer T for the specific task considered.

the kernel K used in our machine translation applications might not be optimal.


, X m be m lattices with the associated references and let x(K, X i ) be the solution of the expected similarity maximization for lattice X i when using kernel K. Then, the kernel learning optimization problem can be formulated as follows: where K is a convex family of rational kernels and Tr[K] denotes the trace of the kernel matrix.

we could choose K as a family of linear combinations of base rational kernels.


17The key idea behind this new approximation algorithm is to rewrite the n-gram posterior probability (Equation 16) as follows: where E X is the set of transitions of X, π x is the unique accepting path labeled by x in X and f (e, w, π) is a score assigned to transition e on path π containing n-gram w: f (e, w, π) =    1 if w ∈ e, p(e|X) > p(e ′ |X), and e ′ precedes e on π 0 otherwise.

(19) for each path π, we count the transition that contributes n-gram w and has the highest transition posterior probability relative to its predecessors on the path π; there is exactly one such transition on each lattice path π.


The key difference between Moses and our system is that, in order to account for target discontinuities, phrases that contains gaps in the target are appended to a partial translation hypothesis in multiple steps.

each translation hypothesis in our decoder is not only represented as a translation prefix and a coverage set as in Moses, but it also contains a set of isolated phrases (shown in italic in Fig.


We made our best attempts to make our system comparable to Moses.

when no discontinuous phrases are provided to our system, it generates an output that is almost identical to Moses (only about 1% of translations differ on average).


In one additional set of experiments, we removed target-discontinuous phrases in Joshua prior to MERT and test time.

we removed all hierarchical phrases whose target side has the form uXv, uXvX, XuXv, and uXvXw, and only allowed rules whose target side has the form uX, Xu, XuX, XXu, or uXX.


Despite these advantages, system combination may be more appropriate in some settings.

model combination is designed primarily for statistical systems that generate hypergraph outputs.


Further detail and discussion of these and other challenges in defining and interpreting requests in email can be found in (Lampert et al., 2008b).

that paper includes analysis of a series of complex edge cases that make even human agreement in identifying requests difficult to achieve.


These top-10 n-grams do not seem to align well with linguistic intuitions, illustrating how the noise from irrelevant message parts hampers performance.

there were several similar, apparently automated messages that were annotated (as non-requests) which appear to be the source of several of the top-10 n-grams.


Another 10% of errors are due to attempts to classify requests in inappropriate genres of email messages.

both marketing messages and spam frequently include request-like, directive utterances which our annotators all agreed would not be useful to mark as re- quests for an email user.


Generally, LSA can be observed as a way to overcome some of the drawbacks of the standard vector space model, such as sparseness and dimensionality.

the LSA similarity is computed in a lower dimensional space, in which second-order relations among words and documents are exploited (Mihalcea et al., 2006).


In this case, much less matches will be counted by the kernel function applied to such parse trees and the one of T4.

the complete VP subtree will not be matched.


The distributional semantics is captured by means of LSA: we used the java Latent Semantic Indexing (jLSI) tool (Giuliano, 2007).

we precomputed the word-pair matrices for RTE2, RTE3, and RTE5.


The dotted line between author and fabula implies that authors sometimes generate the fabula, sometimes not.

an author may re-tell a widely known fabula (e.g., Hamlet); perhaps from a different perspective.


While similar in that these works attempt to make sense of narrative texts, the task at hand is quite different.

narrative schema induction presupposes a single narrative thread.


A related issue that must be addressed is that of deciding when to assign a passage to multiple threads.

given the (estimated) narrative mixtures for each passage as an input, to which (if any) narrative threads ought this passage be assigned?


However, several exceptions suggest that the associations are not straightforward and further research must be done to fully understand the relationship between entrainment, social characteristics and dialogue success.

the explanation behind the associations of entrainment on certain variables with certain social and objective measures is an interesting direction for future work.


Words are labeled as non-shell otherwise.

an individual word w j is labeled as shell (i.e.,ŷ (i) j = 1) according to the following equation, where 1(q) is an indicator function that returns 1 if its argument q is true, and 0 otherwise.


We can also view shell detection as the task of identifying phrases that indicate certain types of speech acts (Searle, 1975).

we aim to identify markers of assertive speech acts, which declare that the speaker believes a certain proposition, and expressive speech acts, which express attitudes toward propositions.


Perhaps most importantly, BP permits the relaxation of artificial constraints that are generally taken for granted as being necessary for efficient inference.

a relatively modest relaxation of the ITG constraint can directly be applied to any model that uses ITG-based inference (e.g.


Instead of tuning a parameter like γ, it would be preferable to let the data dictate the number of entity clusters.

we thus consider a non-parametric Bayesian mixture model where the mixtures are multinomial distributions over the entity contexts S. we consider a DPMM, which automatically infers the number of mixtures.


This paper addresses the extraction of event records from documents that describe multiple events.

we aim to identify the fields of information contained in a document and aggregate together those fields that describe the same event.


For the MUC joint task experiment, in the 10 iterations considered for the majority vote, there is full agreement between the potentials for 97.77% of the unobserved variables.

the voting scheme affects the assignment of only 2.23% of the unobserved variables.


We use Conditional Random Fields (CRF) as our sequence labeling algorithm.

we use first-order chain-structured CRF.


While all of the above-mentioned research has presented intrinsic evaluations of their disengagement modeling efforts that indicate a reasonable degree of accuracy as compared to a gold standard (e.g., manual coding), only a few have yet demonstrated that the model's detected values are useful in practice and/or are a reasonable substitute for the gold standard with respect to some practical objective (e.g., a relationship to performance).

two studies (Bohus and Horvitz, 2009;Schuller et al., 2009a) have gone directly from intrinsic evaluation of (dis)engagement models to performing user studies with the implemented model, thereby bypassing other less expensive and less labor-intensive means of extrinsic evaluation to quantify their model's usefulness-and potentially indicate its need to be further improved-before deployment with real users.


11 Our CC and MLE results are on par with the best results from the state-of-the-art systems competing in the 2010 Interspeech Challenge, where the task was to detect level of interest.

the winner obtained a CC of 0.428 (higher numbers are better) and an MLE of 0.146 (lower numbers are better) (Jeon et al., 2010), while a subsequent study yielded a CC of 0.480 and an MLE of 0.131 on the same corpus (Wang and Hirschberg, 2011).


Because we are adding our disengagement detector to a spoken dialogue system that already detects and adapts to user uncertainty, we argue that it is also necessary to evaluate whether greater performance benefits are likely to be obtained by adapting to a second state.

given how difficult it is to effectively detect and adapt to one user affective state, is performance likely to improve by detecting and adapting to multiple affective states?


14 We previously found a related correlation between different DISE and learning measures, during the analysis of our DISE annotation scheme (Forbes-Riley and Litman, 2011b).

we showed a significant partial correlation between the percentage of manual DISE labels and posttest controlled for pretest score.


In this paper we used extrinsic evaluations to provide evidence for the utility of a new system design involving the complex task of user affect detection, prior to undertaking an expensive and timeconsuming evaluation of an affect-adaptive system with real users.

we first presented a novel model for automatically detecting user disengagement in spoken dialogue systems.


In order to improve the system performance in this ASR condition, we explore the use of word confidence scores from the ASR system during feature generation.

the similarity scores between the test response and each score category are weighted using the recognition confidence score of the response, so that the scores can also contain information related to its acoustic accuracy.


For this task we follow Ghamrawi and McCallum (2005) and Finley and Joachims (2008) and model the label interactions by constructing a fully connected CRF between the output labels.

for every document, we construct a CRF that contains a binary random variable for each label (indicating that the corresponding label is on/off for the document) and one binary edge for every unique pair of labels.


Just as in our own paper , Domke took a decision-theoretic stance and proposed ERM as a way of calibrating the graphical model for use with approximate inference, or for use with data that do not quite match the modeling assumptions.

4 (Domke, 2011) is similar to (Stoyanov et al., 2011) in using ERMA to train model parameters to be used with "truncated" inference that will be run for only a fixed number of iterations.


Our technical challenge is to generalize this MAP criterion to other, structured distributions c that compactly approximate the corpus.

we address the case where c is given by any probabilistic FSA, such as a backoff language model-that is, a variable-order Markov model estimated from corpus statistics.


We take care never to allow backoff to the empty state , 2 since we find that c(w) is otherwise too coarse an approximation to English: sampled sentences tend to be disjointed, with some words generated in complete ignorance of their left context.

the "variational gap" between (4) and 5is the bound is good if q does a good job of approximating p θ 's tagging distribution on a randomly drawn sentence.


7 One could increase the number of arcs and hence variational parameters by splitting the states of cq to remember more history.

one could increase the width nq of the tag window, or one could increase the width of the word window by splitting states of c (without changing the distribution c(w)).


We develop a theoretical framework of "violationfixing" perceptron that addresses these challenges.

we make the following contributions: • We show that, somewhat surprisingly, exact search is not required by perceptron convergence.


2 Exact search is simply used to ensure violation.

if we can guarantee violation in each update (which we call "valid update"), it does not matter whether or how exact the search is.


The concepts of separation/margin, violation, and diameter all change accordingly with this new confusion set.

we say that a dataset D is greedily separable in representation Φ if and only if D, Φ, C g (D) is linearly separable, and we say ( return (x, y, y) ⊲ success (z = y), no violation We now express early update for greedy search (Algorithm 5) in terms of violation-fixing perceptron.


Table 1 shows convergence rates, where max-violation update also converges faster than early and standard methods.

at b = 1, it achieves a 19% error reduction over standard update, while converg-  Figure 5: Percentages of invalid updates for standard update.


While we do treat the metrics as black boxes to an extent, we explicitly chose metrics that were high performing but also complementary in nature.

our re-examination focused on the more sophisticated MT metrics of the last few years that claim to go beyond simple n-gram overlap and edit distance.


Many of these function words do not exist in Classical Chinese.

prepositions are rare 2 ; instead, nouns expressing time, locations, instruments, indirect recipients, etc., modify the verb directly.


Finally, the most frequent reason for removing questions was an extreme imbalance in the answer classifications (e.g., for many questions, almost all of the answers were labeled "partially correct incomplete").

we removed questions where more than 80% of the an-swers had the same label and questions with fewer than three correct answers, since these questions were unlikely to be useful in differentiating between the quality of assessment systems.


In this paper, we focus on improving TM between Arabic and English in more realistic settings, compared to the NEWS workshop dataset.


For large comparable texts, we use contextual clues, namely translations of neighboring words, to constrain TM and to preserve precision.

we initially extract text segments that are "related" based on cross lingual lexical overlap, and then we perform TM on these segments.


Here, we do not make any restrictions on the XTOP besides some sanity conditions (see Section 3).


(2008) because they can be minimized efficiently using an algorithm of Maletti and Quernheim (2011).

wTG can share more than equivalent subtrees and can even represent infinite sets of trees.


This study presents an original approach consisting in titling journalistic articles by nominalizing.

morphological and semantic processing are employed to obtain a nominalized form which has to respect titles characteristics (in particular, relevance and catchiness).


Before applying our system to the task of error correction, we tested its utility in restoring commas in newswire texts.

we evaluate on section 23 of the WSJ, training on sections 02-22.


The projection associated with P QP has a slight twist to this principle, because of the nature of some of the financialspeak in the PTB.

the dollar sign is treated as a displaced word and is therefore not counted, in a QP constituent, as a token for purposes of the "single token" rule.


We also provide a detailed analysis that is interesting from a sociolinguistic standpoint.

what words distinguish non-native writing?


'biomedical'), the table gives a blueprint for writing a solid main-conference paper.

good papers often have an explicit probability model (or algorithm), experimental baselines, error analysis,   Table 7.


(2010) introduced a way of using locality sensitive hashing (LSH) to make this time O(1), while retaining the same accuracy level.

instead of computing the max over the entire set D t , like in (1), they compute it over a smaller set S of potential nearest neighbors.


One problem with using Q as defined in (4) is that it is not very suitable for use in an online setting.

if documents come in one at a time and we have to store each one, only for it to be retrieved at some later point, simply storing them and computing the inner product as in (4) would lead to frequent matrix-vector multiplications.


However, it is clear that not all types of paraphrases are equally helpful.

the automatically extracted paraphrases from  seem to be the most helpful, and by using them our unsupervised system is able to beat even the best known supervised FSD system.


We make some Twitter-specific modification to the baseline system that slightly improve the results.

the baseline uses no stemming, ignores links, @-mentions, and treats hashtags as normal words (i.e., removes the leading '#' character).


A similar Maximum Entropy model is presented by Chen and , where features are the presence or absence of a given character n-gram in w. In our approach, feature functions are defined at character positions rather than over the entire word.

for word w j composed of character sequence c 1 .


After smoothing the probabilities, w Easy (N ) and w Diff (N ) each lie within the interval [0.25, 0.75], and w Easy (N ) + w Diff (N ) = 1.

soft selection with smoothing interpolates the two language models, but assigns a weight of at least 0.25 to each one to reduce the impact of EEG classifier errors.


The third base-line, Weight from ASR (row f) depends solely on ASR results to estimate weights; it performs better than other baselines, but not as well as EEG-based Soft Selection with smoothing (row e).

using EEG alone can weight the two language models better than ASR alone.


Our initial plan for this paper was to use complex language modeling frameworks that allow experimenters to include arbitrary features (including morphological and shape features) in the model.

we looked at publicly available implementations of maximum entropy models (Rosenfeld, 1996;Berger et al., 1996) and random forests (Xu and Jelinek, 2004).


Performing this test with DI-RECTL+ as the base system shows good error rate reduction on names (about 12%) as reported, but a much smaller statistically insignificant error rate re-duction on core vocabulary words (around 2%).

the supplemental transliterations are able to help only for names.


While our SVM reranking approach demonstrates significant improvements for all tasks and all tested base systems, the magnitude of the performance increase is dependent on the base system.

we see a common thread recurring throughout all experiments: SEQUITUR sees higher improvements than does DIRECTL+.


Second, we would like to be able to incorporate general supplemental information rather than being limited by the existence of relevant data.

a good transliteration model should encode a general version of the information provided by a single transliteration, so being able to apply that information would allow us to overcome our dependence on existing data as well as provide more potentially useful information even when a transliteration or transcription already exists.


We observed that different components making up the corpus logarithmic probability have different magnitudes.

we found that the emission probability component in log-scale is roughly four times smaller than the transition probability.


C 3 : Counting token translation pairs In this variant, we count each terminal (source token, target token) pair that survives pruning.

if grammar rule [X]|||f 1 f 2 ...f m |||e 1 e 2 ...e n fires, we increment the count of every pair ⟨f i , e j ⟩, where f i is aligned to e j .


This case illustrates a limitation to fine-grained use of BLEU alone as a basis for analysis, since we might argue that infiltrate into is no less appropriate than sneak into in this context.

some of the reductions we see in BLEU may not be actual errors but rather simply changes that take us outside of the coverage of the test set.


Furthermore, we introduce three tuners that have not been previously tested.


With the exception of MIRA, the tuning approaches discussed in this paper are direct optimizers.

each solves the following optimization problem: where the first term provides regularization, weighted by λ.


Introduced by Hopkins and May (2011), Pairwise Ranking Optimization (PRO) aims to handle large feature sets inside the traditional MERT architecture.

pRO employs a growing approximation of E i by aggregating the k-best hypotheses from a series of increasingly refined models.


To provide a list of candidate expressions with their associated probabilities, we employ a k-best CKY parser.

we implement Algorithm 3 described in Huang and Chiang (2005), providing an O(Gn 3 k log k) algorithm with respect to the grammar size G, phrase length n, and beam size k. We set the beam size to 2000.


The detection model is implemented as a Conditional Random Field (Lafferty et al., 2001), with features over the morphology and context.

we define the following features: • The word and lemma within 2 of the current word.


The best model obtains an f-measure of 68.84, calculated by considering exact matches between chunks.

unless the model predicts as fine-grained focus exactly the actual fine-grained focus, it is considered wrong when calculating performance.


Caraballo (1999) was the first to induce a taxonomy from a corpus using a combination of clustering and pattern-based methods.

nouns are organized into a tree using a bottom-up clustering algorithm and internal nodes of the resulting tree are labeled with hypernyms from the nouns clustered underneath using patterns such as "B is a kind of A".


The nouns and their class labels were further taxonomized using WordNet (Fellbaum, 1998).

we first identified the full hypernym path in WordNet for each noun in McRae et al.


This suggests that the algorithm is highly dependent on the quality of the semantic network used as input.

hRGs are known to be more appropriate for so-called small-world networks, graphs composed of densely-connected subgraphs with relatively sparse connections between (Klapaftis and Manandhar, 2010).


the BNC semantic network, using a combination of the baseline clustering methods evaluated in Experiment 2.

we first obtain a (flat) clustering using either CW or Brown, which we then use to re-weight the BNC graph given as input to the HRG.


Our experiments have also shown that the topology of the input semantic network is critical for the success of the HRG.

edge re-weighting plays an important role and generally improves performance.


Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction.

we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%.


In this study we focus on semi-supervised and linguistic-transfer methods for multilingual structure prediction.

we pursue two lines of research around the use of word cluster features in discriminative models for structure prediction: 1.


(2011) relies on these three assumptions.

by replacing fine-grained language specific part-of-speech tags with universal part-of-speech tags, generated with the method described by , a universal parser is achieved that can be applied to any language for which universal part-of-speech tags are available.


Then, the algorithm iterates over unlabeled sentences and asks only for a single bit of feedback, rather than a full parse tree.

given an example the algorithm outputs two possible parse trees and receives only a single bit indicating which of the two alternatives has more correct edges.


After building an initial classifier, our algorithm reduces the work of the annotator from a full annotation of the input sentence to a single bit of information.

it provides the annotator with two alternative parses of the input sentence and asks for the single bit indicating which of the alternatives is better.


We thus further restrict the possible feedback requested from the annotator.

given a new sentence our algorithm outputs two possible annotations, or parse trees,ŷ A andŷ B , and asks for a single bit from the annotator, indicating whether parse A is better or parse B is better.


The two parses our algorithms presents to the annotator are the highest ranking parse and the second highest ranking parse according to the current model.

the parse it would output for x and the best alternative.


In fact, we found empirically that, on average, in 95% of the sentences, they differ in the labeling of only a single word.

botĥ y A andŷ B agree on all words, except some word x i , for which the first alternative assigns to some word x j and the second alternative assign to other word x k .


First, we used the smaller set to build a parser using standard supervised learning procedure.

we used MSTParser and ran the MIRA online learning algorithm for 5 iterations.


Instead, we turn to the linguistic form of an utterance.

the utterer can choose linguistic forms in her utterance to signal that she is imposing further constraints on the addressee's choice of how to respond, constraints which go beyond those defined by the standard set of dialog acts.


We propose an elegant and automatically tunable alternative for the Shallow-n grammars setting.

we introduce a BITG-style reordering glue rule ( § 3) and a monotonic X-glue rule ( § 4).


It turns out that the annotators exhibited very non-uniform behavior in this respect.

activity of action (volunteer labeled words) follows a power law, and many subjects volunteered no features at all.


We were able to partially replicate the results from Settles (2011).

for two of the same data sets, some of the subjects using DUALIST significantly outperformed those using traditional document-only interfaces.


beling and/or volunteering features), understanding how these options impact learning becomes more important.

training a good classifier in our experiments appears to be linked to (1) volunteering more labeled words, and (2) maintaining a class balance among them.


However, it seems unrealistic to allow an arbitrary number of unchanged words in an edit.

we want to avoid very large edits that cover complete sentences.


We adapt methods proposed by Mejer and Crammer (2010) in order to produce per-edge confidence estimations in the prediction.

one approach is based on sampling, and another on a generalization of the concept of margin.


The second method, named Weighted K-Best (WKB), is a deterministic method building on properties of the inference algorithm.

we use k-best Maximum Spanning Tree algorithm (Hall, 2007) to produce the K parse trees with the highest score.


On the other hand, if the score of KD-Fix is the same, as happens for many edges at high recall values, then Delta breaks arbitrary ties.

the new method first ranks edges according to the confidence score of KD-Fix, then among edges with equal KD-Fix confidence score a secondary order is employed using Delta.


After collection and categorization, all records go through a cleaning process that starts with the re- The Record Linkage process should link together all records belonging to the same real-world person.

this process should turn billions of input records into a few hundred million clusters of records (or profiles), where each cluster is uniquely associated with a real-world unique individual.


The Conditional Alias Probability (CAP) of an alias defines the probability of an alias being used to denote a person with a given name.

it can be expressed as CAP (alias i |name j ) = count(alias i ∧name j ) count(name j ) , where the count() operator returns the number of profiles satisfying its criteria.


In contrast to EP08, however, they use second order vectors as basic representation for the target word.

the vector for a target word w has components for all combinations of two grammatical roles r, r and a context word w ; the inner sum gives the value for each component.


We now show that the models are essentially equivalent if we ignore syntax: they component-wise multiply the second order vector of one word (target or context) with the first order vector of the other word.

we obtain the following derivations, where W = {w 1 , ..., w n } denotes the vocabulary, and V the symmetric n × n input matrix, where gives the co-occurrence association between words w i and w j : where < v, w> denotes scalar product.


treatment of syntax in the line of the much simpler proposal of TFP11.

: where V is a I × J syntactic input matrix, i.e.


In this paper we investigated the usefulness of heterogeneous information sources in improving measures of semantic word relatedness.

we created vector space models using 4 data sources from 3 categories (corpus-based, Web-based and thesaurus-based) and found that simply averaging the cosine similarity derived from these models yields a very robust measure.


The average ROUGE-2 scores were 0.2575 weighted and 0.2362 unweighted, a statistically significant increase with a 95% confidence interval of 0.0066 to 0.0360.

we see significant improvements in articles that contain sentences TextRank ranked highly but have events occurring at significantly different times than the central event of the article.


In recent years (Belz et al., 2010) there has been a shift towards what we term "Open Domain Referring Expression Generation," (OD REG), that is, a REG task where the properties come from a folksonomy, a large-scale volunteer-built ontology.

we are interested in changing anaphoric references for entities appearing in sentences drafted from different documents, as done in multi-document summarization (Advaith et al., 2011).


We used latent Dirichlet allocation (LDA) (Blei, Ng, and Jordan, 2003) as our exploratory tool.

we ran a collapsed Gibbs sampling implementation of LDA (Griffiths and Steyvers, 2004).


That is the motivation for combining them into a trie structure and labeling them in such a way that the path remains a flow until there is no path element that leads to a flow leaf (Figure 2).

we want the path elements common to a flow path and an inert path to reinforce sentiment flow.


The accuracy measure, however, does show overall improvement with the inclusion of more featurefactor combinations.

the node-child h factor does appear to have an effect on the performance.


First, we relax the assumption made by most previous work (Zhu and Ghahramani, 2002;Baluja et al., 2008;Subramanya and Bilmes, 2008;Subramanya and Bilmes, 2009;Subramanya et al., 2010;Das and Petrov, 2011;Das and Smith, 2011) that the 1 norm of the masses assigned to the labels for a given vertex must be 1.

in our framework, the label distribution at each vertex is unnormalized-the only constraint we put on the vertices' vectors is that they must be nonnegative.


Both are inspired by previous work; however, the use of the JS divergence is a novel extension to Subramanya and Bilmes (2008).

the factors are: where µ is a hyperparameter whose choice we discuss in §4.


Given this formulation, EM can be shown to maximize F via block coordinate ascent alternating over q (E-step) and θ (M-step) (Neal and Hinton, 1998).

the E-step for EM can be written as where Q is the space of all distributions.


A key observation underlying the development of UEM is that hard EM (or CoDL) finds a distribution with zero entropy while EM (or PR) finds a distribution with the same entropy as P θ (or close to it).

we modify the objective of the E-step of EM 3as where KL(q, p; γ) is a modified KL divergence: In other words, UEM projects P θ (h|x) on the space of feasible distributions Q w.r.t.


This notion aligns with the observation of (Spitkovsky et al., 2010) who criticize EM for frittering away too much probability mass on unimportant outputs while showing that hard EM does much better in PCFG parsing.

they empirically show that when initialized with a "good" set of parameters obtained by supervised learning, EM drifts away (thus losing accuracy) much farther than hard-EM.


Suppose our task is to predict two output variables h 1 and h 2 coupled via linear constraints.

they obey U e h 1 = U e h 2 (agreement constraints) and U i h 1 ≤ U i h 2 (inequality constraints) 4 for given matrices U e and U i .


The optimization problem corresponding to the generative model turns out to be identical to that of canonical correlation analysis (CCA) (Hotelling, 1936;Hardoon et al., 2004), which immediately suggests a solution by solving an eigensystem.

the projection directions are obtained by solving the following generalized eigensystem: where xy , τ is a regularization parameter and I is the identity matrix of appropriate size.


9), enables a simpler objective function whose optimum can be derived by following a procedure very similar to that of the generative model.

the projection directions are obtained by solving Eq.


6 is given by: Differentiating the Lagrangian with respect to the parameters a, b and setting them to zero yields the solution for the parameters in terms of the Lagrangian multipliers α ij as follows: where C α xy = X 1−λ λ Y T + R T and R is a matrix of size d 2 × n with i th column as given by We use superscript α on the crosscovariance matrix to indicate that it is dependent on the Lagrangian multipliers α ij .

the solution is similar to that of the previous formulation except that the residual vectors are weighted by the Lagrangian multipliers instead of the loss function.


In this paper, except for Chinese, we use suffixes of length two to four as features in the word view and unigram and bigram tag sequences as features in the tag view.

we convert each word of the sentence into suffixes of length two to four and then treat each sentence as a bag of suffixes.


16 represent that any input and output pair should have at least a margin of 1 (modulo slack), whereas in our approach, the constraints include incorrect outputs along with their loss value.

our formulation is more suitable for the reranking problem while Eq.


Motivated by this observation, this paper investigates the novel task of self-assessment of autocorrection performance based on interactions between dialogue participants.

we formulate this task as the automatic identification of correction mistakes and their corresponding intended words based on the discourse.


By thresholding our results to only trust predictions in which the ranker reported a high ranking value for the top term, we were able to examine the precision at different recall levels.

if the top ranked term does not meet the threshold, we simply do not predict an intended term for that instance, hurting recall but hopefully improving precision by removing instances that we are not confident about.


Although supervised approaches work reasonably well, they present a challenge to deploying coreference technologies across a large number of natural languages.

for each new language of interest, one has to hire native speakers of the language to go through the labor-intensive, timeconsuming process of hand-annotating a potentially large number of documents with coreference annotation before a supervised resolver can be trained.


However, by adopting these approaches, we are simply replacing the corpus annotation bottleneck by another, possibly equally serious, bottleneck, the knowledge acquisition bottleneck.

in these approaches, one has to employ knowledge of the target language to design coreference rules (e.g., Mitkov (1999), Poon and Domingos (2008), Raghunathan et al.


There have been two initial attempts to apply projection to create coreference-annotated data for a resource-poor language, both of which involve projecting hand-annotated coreference data from English to Romanian via a parallel corpus.

harabagiu and Maiorano (2000) create an English-Romanian corpus by manually translating the MUC-6 corpus into Romanian and manually project the English annotations to Romanian.


Hence, all we can do is to employ the three steps involved in the projection approach as described at the beginning of this section to create coreference-annotated data for French.

we translate a French text to an English text using GoogleTranslate 1 , and create coreference chains for the translated English text using Reconcile 2 (Stoyanov et al., 2010).


Note that the use of a supervised resolver like Reconcile does not render our approach supervised, since we can replace it with any resolver, be it supervised, heuristic, or unsupervised.

we treat the resolver built for the source language as a black box that can produce coreference annotations.


After that, we create a mapping between the English mentions produced by the NP projection algorithm and those produced by Reconcile using a small set of heuristics.

let M P be the set of mentions identified by the NP projection algorithm and M R be the set of mentions identified by Reconcile.


We assume that in addition to a French mention extractor, we have access to other French linguistic taggers (e.g., syntactic and semantic parsers) that will allow us to generate the linguistic features needed to train a French resolver on the projected coreference annotations.

assume that Test is a set of French texts we want to coreference-annotate, and Training is a set of French texts that is disjoint from Test but is drawn from the same domain as Test.


's (2001) method for creating training instances.

we create (1) a positive instance for each anaphoric mention m k and its closest antecedent m j ; and (2) a negative instance for m k paired with each of the intervening mentions, m j+1 , m j+2 , .


After training, the classifier is used to identify an antecedent for a mention in a test text.

each mention, m k , is compared to each preceding mention, m j , from right to left, and m j is selected as the antecedent of m k if the pair is classified as coreferent.


The format of each data set follows that of a typical CoNLL shared task data set.

each row corresponds to a word in a document; moreover, all but the last column contain the linguistic features computed for the words, and the last column stores the coreference information.


According to our cross-validation experiments on the training data, the best heuristic for extracting Spanish mentions is different from that for extracting Italian mentions.

for Spanish, the best heuristic method operates as follows.


While our MT-based projection approach is unsupervised (i.e., it does not rely on any coreference annotations from the target language), it would be informative to see the performance of the supervised resolvers, since their performance can be viewed as a crude upper bound on the performance of our unsupervised systems.

we train a mention-pair model on the training set using the 23 features shown in Table 1 and SVM light as the underlying learning algorithm 12 , and apply the resulting model in combination with Soon et al.


These results provide evidence for our earlier hypothesis that our projection algorithm can profitably exploit the linguistic knowledge about the target language that is available to it.

the mention extractor helps make our approach less sensitive to word alignment and NP projection errors.


Finally, we evaluate our approach in a setting where it has access to all the information available to our supervised resolvers, except for the gold-standard coreference annotations on the training sets.

our approach uses projected coreference annotations to train a resolver on the training texts, whereas the supervised resolvers do so using gold-standard annotations.


This suggests that the classifiers being trained in Setting 3 have enabled the discovery of additional coreference links.

there are benefits to be obtained just by learning over noisy coreference annotations, a result that we believe is quite interesting.


To achieve our goal of selecting better gap-fill questions, we have broken down the task into stages similar to those proposed by Nielsen (2008): 1) sentence selection, 2) question construction, and 3) classification/scoring.

we utilize summarization to identify key sentences from a passage.


We do this by abstracting the structure of the database (and accompanying texts) into a PCFG whose probabilities are learned from training data.

1 we convert the database into rewrite rules and represent them as a weighted directed hypergraph (Gallo et al., 1993).


In both corpora, nouns tend not to have any modifiers, but modifiers are still more prevalent in Flickr than in generic text.

60% of nouns in Flickr have zero modifiers, but 70% of nouns in generic text have zero modifiers.


One immediate question raised in this process is whether one needs to actually see the image to perform the annotation.

if we expect an NLP system to be able to classify noun phrases as visual or non-visual, we need to know whether people can do this task sans image.


Moreover, we assume that each target word t ∈ T s has a set of senses in common with s. These senses may also be shared among different target words.

each target word may have multiple senses and each sense may be expressed by multiple words.


We identify these examples among the extracted phrase pairs of a parallel corpus.

let P s be the set of source phrases containing source word s, and let A t be the set of source phrases that align to target phrases containing target word t. For a source word s and target sense cluster G, we identify source phrases that contain s and translate to all words in G. we collect the set of phrases P s ∩ t∈G A t .


Our approach to predicting a bill's survival is logistic regression.

let X be a random variable associated with a bill, and let f be a feature vector function that encodes observable features of the bill.


A related observation is that held-out perplexity does not seem to predict BLEU in any useful way.

perplexity does not predict that the two systems will become similar as data grows, nor does it predict that maxent's performance will level off.


The last row of Table 2 (All Pairs) shows the results when requiring matches from at least two different bootstrapped dictionaries.

we labeled a document as relevant if it contained at least one phrase from each of two different dictionaries and these phrases occurred in the same sentence.


Bilingual NER experiments on the large OntoNotes 4.0 Chinese-English corpus show that the proposed method can improve strong baselines for both Chinese and English.

chinese performance improves by over 5% absolute F 1 score.


This is likely to be because: 1) Soft-align includes more word alignment pairs, thereby improving recall; and 2) uses probabilities to cut wrong word alignments, thereby improving precision.

compared with the strong CRF baseline, the gain on Chinese side is almost 5.5% in absolute F 1 score.


Each occurrence of Diff is weighted by the localSim of the phrase pair in which Diff occurs.

globalSim is defined as: Threshold The frequency threshold of Table 2 (Section 2.1.2).


We compared our method with the state-of-theart supervised methods proposed by , using their WCL datasets v1.0 (http: //lcl.uniroma1.it/wcl/), definition and nondefinition datasets for English .

we used its training data (T rDat wcl , hereafter), which consisted of 1,908 definition and  2,711 non-definition sentences, and compared the following three methods.


Moreover, we can improve accuracy by simultaneously operating on relations observed in the New York Times corpus and in Freebase.

our model outperforms the current state-of-the-art distant supervision method (Surdeanu et al., 2012) by 10% points Mean Average Precision through joint implicature among surface patterns and Freebase relations.


(2009) and employ Stochastic Gradient Descent (SGD).

in each epoch we sample |O| facts with replacement from O.